How One IT Solutions built a machine learning-powered predictive analytics platform that identified at-risk customers 30 days in advance, enabling proactive retention and reducing churn by 40%.
A B2B SaaS company with 8,000 subscribers was losing 8% of customers monthly without warning. Customer success teams had no data to prioritise outreach. The company needed a system to predict churn risk 30 days out and surface actionable signals for each at-risk account.
We built a churn prediction pipeline using XGBoost trained on 18 months of usage data, support tickets, billing events, and feature adoption signals. The model scores all active accounts daily and pushes risk scores into a Tableau dashboard and Slack alerts. Feature importance analysis tells CSMs exactly why each account is flagged.
Every feature was designed to solve a specific business problem—not just to add complexity.
XGBoost model trained on usage, billing, and support data—predicts churn risk 30 days in advance with 87% accuracy.
SHAP values explain every prediction—CSMs know exactly which product areas are driving risk for each account.
Tableau dashboards with cohort analysis, revenue at risk, and retention trend visualisations.
Daily Slack digests of newly at-risk accounts ranked by predicted revenue impact and recommended action.
Apache Spark ETL pipeline refreshes all signals daily from CRM, product DB, and billing system.
Centralised Redshift warehouse unifying all customer data for ad-hoc analysis and model retraining.
Scalable cloud-native architecture designed for high availability, horizontal scaling, and zero-downtime deployments.
End-to-end encryption, role-based access control, automated security scanning, and regular penetration testing built into the SDLC.
Agile sprints with weekly demos, CI/CD pipeline via GitHub Actions, and comprehensive documentation handed over at project close.
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